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add: simulation scripts

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This commit is contained in:
Daniel Kapla 2021-04-20 20:04:59 +02:00
parent 2c5a87ccfe
commit 0214823794
5 changed files with 260 additions and 26 deletions
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1
.gitignore vendored
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@ -16,6 +16,7 @@ NNSDR/man/
*.Rdata
*.zip
*.tar.gz
*.tar.xz
*.BAK
# LaTeX - build/database/... files

28
NNSDR/R/datasets.R
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@ -181,17 +181,7 @@ rlaplace <- function(n = 1, mu = 0, sd = 1) {
#' \eqn{e_j} is the \eqn{j}-th unit vector in the \eqn{p}-dimensional space.
#' \eqn{Y} is given as \deqn{Y = (b_1'X)^2+(b_2'X)^2+(b_3'X)^2+0.5\epsilon}
#' where \eqn{\epsilon} is standard normal.
#' @section M7:
#' The predictors are distributed as \eqn{X\sim t_3(I_p)}{X~t_3(I_p)} where
#' \eqn{t_3(I_p)} is the standard multivariate t-distribution with 3 degrees of
#' freedom, for a subspace dimension of \eqn{k = 4} with a default of
#' \eqn{n = 200} data points.
#' \eqn{p = 20, b_1 = e_1, b_2 = e_2, b_3 = e_3}, and \eqn{b_4 = e_p}, where
#' \eqn{e_j} is the \eqn{j}-th unit vector in the \eqn{p}-dimensional space.
#' \eqn{Y} is given as \deqn{Y = (b_1'X)(b_2'X)^2+(b_3'X)(b_4'X)+0.5\epsilon}
#' where \eqn{\epsilon} is distributed as generalized normal distribution with
#' location 0, shape-parameter 1, and the scale-parameter is chosen such that
#' \eqn{Var(\epsilon) = 0.25}.
#' @section M7: see "Local Linear Forests" <arXiv:1807.11408>
#'
#' @import stats
#' @importFrom stats rnorm rbinom
@ -253,18 +243,9 @@ dataset <- function(name = "M1", n = NULL, p = 20, sd = 0.5, ...) {
X <- matrix(rnorm(n * p), n)
Y <- rowSums((X %*% B)^2) + rnorm(n, 0, sd)
} else if (name == "M7") {
if (missing(n)) { n <- 400 }
# B ... `p x 4`
B <- diag(p)[, -(4:(p - 1))]
# "R"andom "M"ulti"V"ariate "S"tudent
X <- rmvt(n = n, sigma = diag(p), df = 3)
XB <- X %*% B
Y <- (XB[, 1]) * (XB[, 2])^2 + (XB[, 3]) * (XB[, 4])
Y <- Y + rlaplace(n, 0, sd)
} else if (name == "M8") {
# see: "Local Linear Forests" <arXiv:1807.11408>
if (missing(n)) { n <- 600 }
if (missing(p)) { p <- 20 } # 10 and 50 in "Local Linear Forests"
if (missing(p)) { p <- 20 } # 10 and 50 in "Local Linear Forests"
if (missing(sd)) { sd <- 5 } # or 20
B <- diag(1, p, 4)
@ -274,11 +255,6 @@ dataset <- function(name = "M1", n = NULL, p = 20, sd = 0.5, ...) {
XB <- X %*% B
Y <- 10 * sin(pi * XB[, 1] * XB[, 2]) + 20 * (XB[, 3] - 0.5)^2 + 5 * XB[, 4] + rnorm(n, sd = sd)
Y <- as.matrix(Y)
} else if (name == "M9") {
if (missing(n)) { n <- 300 }
X <- matrix(rnorm(n * p), n, p)
Y <- X[, 1] + (0.5 + X[, 2])^2 * rnorm(n)
B <- diag(1, p, 2)
} else {
stop("Got unknown dataset name.")
}

48
simulations/colin_test.R Normal file
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@ -0,0 +1,48 @@
library(MAVE)
library(CVarE)
library(NNSDR)
set.seed(797)
dataset <- function(n = 100, p = 10, sd = 0.5) {
X <- matrix(rnorm(n * (p - 1)), n, p - 1)
X <- cbind(-0.5 * (X[, 1] + X[, 2]) + 0.001 * rnorm(n), X)
B <- diag(p)[, 4, drop = FALSE]
Y <- as.matrix(X[, 4]^2 + rnorm(n, 0, sd))
return(list(X = X, Y = Y, B = B))
}
nn <- nnsdr$new(
input_shapes = list(x = 10L),
d = 1L,
hidden_units = 512L,
activation = 'relu',
trainable_reduction = TRUE
)
sim <- data.frame(mave = rep(NA, 10), cve = NA, nn.opg = NA, nn.ref = NA)
for (i in 1:10) {
cat(i, "/ 10\n")
with(dataset(), {
dr <- mave.compute(X, Y, method = 'meanMAVE', max.dim = 1)
sim[i, 'mave'] <<- dist.subspace(B, coef(dr, 1), normalize = TRUE)
dr <- cve.call(X, Y, k = 1)
sim[i, 'cve'] <<- dist.subspace(B, coef(dr, 1), normalize = TRUE)
nn$fit(X, Y, epochs = c(200L, 400L), batch_size = 32L, initializer = 'fromOPG')
sim[i, 'nn.opg'] <<- dist.subspace(B, coef(nn, 'OPG'), normalize = TRUE)
sim[i, 'nn.ref'] <<- dist.subspace(B, coef(nn), normalize = TRUE)
})
nn$reset()
}
round(t(data.frame(
mean = colMeans(sim),
median = apply(sim, 2, median),
sd = apply(sim, 2, sd)
)), 3)
# &$\mave$& $\cve$&$\nn_{512}$ \\
# mean & 0.917 & 0.164 & 0.101 \\
# median & 0.999 & 0.162 & 0.096 \\
# sd & 0.256 & 0.057 & 0.032 \\

98
simulations/simulations.R Normal file
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@ -0,0 +1,98 @@
#!/usr/bin/env Rscript
library(MAVE)
library(CVarE)
Sys.setenv(TF_CPP_MIN_LOG_LEVEL = "3") # Suppress `tensorflow` notes/warnings
suppressPackageStartupMessages({
library(NNSDR)
})
## Parse script parameters
args <- parse.args(defaults = list(
# Simulation configuration
reps = 100, # Number of replications
dataset = '1', # Name (number) of the data set
# Neuronal Net. structure/definitions
hidden_units = 512L,
activation = 'relu', # or `relu`
trainable_reduction = TRUE,
# Neuronal Net. training
epochs = c(200L, 400L), # Number of training epochs for (`OPG`, Refinement)
batch_size = 32L,
initializer = 'fromOPG',
seed = 1390L
))
## Generate reference data (gets re-sampled for each replication)
ds <- dataset(args$dataset) # Generates a list with `X`, `Y`, `B` and `name`
## Build Dimension Reduction Neuronal Network model (matching the data)
nn <- nnsdr$new(
input_shapes = list(x = ncol(ds$X)),
d = ncol(ds$B),
hidden_units = args$hidden_units,
activation = args$activation,
trainable_reduction = args$trainable_reduction
)
## Open simulation log file, write simulation configuration and header
log <- file(format(Sys.time(), "results/sim_%Y%m%d_%H%M.csv"), "w", blocking = FALSE)
cat(paste('#', names(args), args, sep = ' ', collapse = '\n'), '\n',
'method,replication,dist.subspace,dist.grassmann,mse\n',
sep = '', file = log, append = TRUE)
## Set seed for sampling simulation data (NOT effecting the `NN` initialization)
set.seed(args$seed)
## Repeated simulation runs
for (rep in seq_len(args$reps)) {
## Re-sample seeded data for each simulation replication
with(dataset(ds$name), {
## Sample test dataset
ds.test <- dataset(ds$name, n = 1000)
## First the reference method `MAVE`
dr <- mave(Y ~ X, method = "meanMAVE")
d.sub <- dist.subspace(B, coef(dr, ncol(B)), normalize = TRUE)
d.gra <- dist.grassmann(B, coef(dr, ncol(B)))
mse <- mean((predict(dr, ds.test$X, dim = ncol(B)) - ds.test$Y)^2)
cat('"mave",', rep, ',', d.sub, ',', d.gra, ',', mse, '\n', sep = '',
file = log, append = TRUE)
## and the `OPG` method
dr <- mave(Y ~ X, method = "meanOPG")
d.sub <- dist.subspace(B, coef(dr, ncol(B)), normalize = TRUE)
d.gra <- dist.grassmann(B, coef(dr, ncol(B)))
mse <- mean((predict(dr, ds.test$X, dim = ncol(B)) - ds.test$Y)^2)
cat('"opg",', rep, ',', d.sub, ',', d.gra, ',', mse, '\n', sep = '',
file = log, append = TRUE)
## Next the `CVE` method
dr <- cve(Y ~ X, k = ncol(B))
d.sub <- dist.subspace(B, coef(dr, ncol(B)), normalize = TRUE)
d.gra <- dist.grassmann(B, coef(dr, ncol(B)))
mse <- mean((predict(dr, ds.test$X, k = ncol(B)) - ds.test$Y)^2)
cat('"cve",', rep, ',', d.sub, ',', d.gra, ',', mse, '\n', sep = '',
file = log, append = TRUE)
## Fit `NNSDR` model
nn$fit(X, Y, epochs = args$epochs,
batch_size = args$batch_size, initializer = args$initializer)
# `OPG` estimate
d.sub <- dist.subspace(B, coef(nn, 'OPG'), normalize = TRUE)
d.gra <- dist.grassmann(B, coef(nn, 'OPG'))
cat('"nn.opg",', rep, ',', d.sub, ',', d.gra, ',', NA, '\n', sep = '',
file = log, append = TRUE)
# Refinement estimate
d.sub <- dist.subspace(B, coef(nn), normalize = TRUE)
d.gra <- dist.grassmann(B, coef(nn))
mse <- mean((nn$predict(ds.test$X) - ds.test$Y)^2)
cat('"nn.ref",', rep, ',', d.sub, ',', d.gra, ',', mse, '\n', sep = '',
file = log, append = TRUE)
})
## Reset model
nn$reset()
}
## Finished, close simulation log file
close(log)

111
simulations/simulations_bigdata.R Normal file
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@ -0,0 +1,111 @@
#!/usr/bin/env Rscript
library(MAVE)
library(CVarE)
Sys.setenv(TF_CPP_MIN_LOG_LEVEL = "3") # Suppress `tensorflow` notes/warnings
suppressPackageStartupMessages({
library(NNSDR)
})
## Parse script parameters
args <- parse.args(defaults = list(
# Simulation configuration
reps = 10L, # Number of replications
dataset = '6', # Name (number) of the data set
# Neuronal Net. structure/definitions
hidden_units = 512L,
activation = 'relu',
trainable_reduction = TRUE,
# Neuronal Net. training
epochs = c(200L, 400L), # Number of training epochs for (`OPG`, Refinement)
batch_size = 32L,
initializer = 'fromOPG',
# Simulation data generation configuration
seed = 1390L,
n = 100L,
p = 20L
))
## Generate reference data (gets re-sampled for each replication)
# Number of observations are irrelevant for the reference to generate a matching
# `NNSDR` estimator
ds <- dataset(args$dataset, n = 100L, p = args$p) # Generates a list with `X`, `Y`, `B` and `name`
## Build Dimension Reduction Neuronal Network model (matching the data)
nn <- nnsdr$new(
input_shapes = list(x = ncol(ds$X)),
d = ncol(ds$B),
hidden_units = args$hidden_units,
activation = args$activation,
trainable_reduction = args$trainable_reduction
)
## Open simulation log file, write simulation configuration and header
log <- file(format(Sys.time(), "results/sim_big_%Y%m%d_%H%M.csv"), "w", blocking = FALSE)
cat(paste('#', names(args), args, sep = ' ', collapse = '\n'), '\n',
'method,replication,dist.subspace,dist.grassmann,mse,time.user,time.system,time.elapsed\n',
sep = '', file = log, append = TRUE)
## Repeated simulation runs
for (rep in seq_len(args$reps)) {
## Re-sample seeded data for each simulation replication
with(dataset(ds$name, n = args$n, p = args$p), {
## Sample test dataset
ds.test <- dataset(ds$name, n = 1000L, p = args$p)
## First the reference method `MAVE`
# To be fair for measuring the time, set `max.dim` to true reduction dimension
# and with `screen = ncol(X)` screening is turned "off".
time <- system.time(dr <- mave.compute(X, Y, max.dim = ncol(B),
method = "meanMAVE", screen = ncol(X)))
d.sub <- dist.subspace(B, coef(dr, ncol(B)), normalize = TRUE)
d.gra <- dist.grassmann(B, coef(dr, ncol(B)))
mse <- mean((predict(dr, ds.test$X, dim = ncol(B)) - ds.test$Y)^2)
cat('"mave",', rep, ',', d.sub, ',', d.gra, ',', mse, ',',
time['user.self'], ',', time['sys.self'], ',', time['elapsed'], '\n',
sep = '', file = log, append = TRUE)
## and the `OPG` method
time <- system.time(dr <- mave.compute(X, Y, max.dim = ncol(B),
method = "meanOPG", screen = ncol(X)))
d.sub <- dist.subspace(B, coef(dr, ncol(B)), normalize = TRUE)
d.gra <- dist.grassmann(B, coef(dr, ncol(B)))
mse <- mean((predict(dr, ds.test$X, dim = ncol(B)) - ds.test$Y)^2)
cat('"opg",', rep, ',', d.sub, ',', d.gra, ',', mse, ',',
time['user.self'], ',', time['sys.self'], ',', time['elapsed'], '\n',
sep = '', file = log, append = TRUE)
## Next the CVE method
time <- system.time(dr <- cve.call(X, Y, k = ncol(B)))
d.sub <- dist.subspace(B, coef(dr, ncol(B)), normalize = TRUE)
d.gra <- dist.grassmann(B, coef(dr, ncol(B)))
mse <- mean((predict(dr, ds.test$X, k = ncol(B)) - ds.test$Y)^2)
cat('"cve",', rep, ',', d.sub, ',', d.gra, ',', mse, ',',
time['user.self'], ',', time['sys.self'], ',', time['elapsed'], '\n',
sep = '', file = log, append = TRUE)
## Fit `DR` Neuronal Network model
time <- system.time(nn$fit(X, Y, epochs = args$epochs,
batch_size = args$batch_size, initializer = args$initializer))
# OPG estimate
d.sub <- dist.subspace(B, coef(nn, 'OPG'), normalize = TRUE)
d.gra <- dist.grassmann(B, coef(nn, 'OPG'))
cat('"nn.opg",', rep, ',', d.sub, ',', d.gra, ',NA,NA,NA,NA\n',
sep = '', file = log, append = TRUE)
# Refinement estimate
d.sub <- dist.subspace(B, coef(nn), normalize = TRUE)
d.gra <- dist.grassmann(B, coef(nn))
mse <- mean((nn$predict(ds.test$X) - ds.test$Y)^2)
cat('"nn.ref",', rep, ',', d.sub, ',', d.gra, ',', mse, ',',
time['user.self'], ',', time['sys.self'], ',', time['elapsed'], '\n',
sep = '', file = log, append = TRUE)
})
## Invoke the garbage collector
gc()
## Reset model
nn$reset()
}
## Finished, close simulation log file
close(log)